System and method for processing low signal-to-noise ratio signals

ABSTRACT

A system and method for use in a real time system and for processing a signal with a low signal-to-noise ratio (SNR). The system comprises a model for modeling an expected signal and a filter that uses the model for filtering the signal. The filter is used for generating a prediction of the signal and an error variance matrix. The system further comprises an adaptive element for modifying the error variance matrix such that the bandwidth of the filter is widened, wherein the filter behaves like an adaptive filter.

[0001] The present invention relates generally to a system and methodfor processing signals with low signal-to-noise ratios (SNRs), andparticularly to physiological signals with low SNRS.

BACKGROUND OF THE INVENTION

[0002] In certain applications it is necessary to analyze physiologicalsignals which are contaminated with noise. These signals often have lowamplitudes, which results in a poor signal-to-noise ratio (SNR). A poorSNR causes difficulty in signal processing and requires complex, lengthyalgorithms for processing the signals with accuracy. In some cases, notonly does the physiological signal occur with poor SNRs, but also thestimuli that elicit such a physiological signal are of the same mode, ornature, as the signal. Such stimuli can affect the data acquisitionprocess or contaminate the signal.

[0003] Such a problem is demonstrated in current methods used to test anindividual's hearing. It is known that the introduction to the ear canalof an acoustic stimulus results in the production of numerous audibleintermodulation distortion products. The acoustic stimulus comprises twosingle frequency sinusoidal tones, called primaries, at frequencies f₁and f₂ with the levels of about 30 to 75 dB Sound Pressure Level (SPL).A normal inner ear will then produce sinusoidal mechanical responses atadditional frequencies, the stronger component of which occurs atfrequency 2f₁-f₂ (the cubic Distortion Product Otoacoustic Emission,DPOAE). This energy is transferred by the middle ear back into the earcanal where it appears as an acoustic signal. The origin of DPOAE liesin the mechanical non-linearity of the cochlea due to internal activeprocesses, associated with the motility of the outer hair cells. Thephenomenon is intrinsic to the normally functioning inner ear. Thus, thepresence or absence of DPOAE provides strong evidence of inner earfunction (or dysfunction), making it a valuable diagnostic and screeningtool.

[0004] However, detection of a DPOAE signal is difficult because itslevel is very low (that is the sound is very soft). and is typicallybetween minus 15 and plus 10 dB SPL. As a consequence of backgroundphysiological, acoustic, and instrumentation noise, which is typicallyabout 30 to 50 dB SPL, the signal-to-noise ratio is very poor.

[0005] Several solutions have been proposed thus far, two of which aredescribed in U.S. Pat. No. 5,413,114 (which is a divisional of U.S. Pat.No. 5,267,571) and U.S. Pat. No. 5,664,577 (which is a continuation ofU.S. Pat. No. 5,526,819). The contents of these references areincorporated herein by reference. U.S. Pat. No. 5,413,114 teaches asystem and method for testing hearing by presenting multiple singlefrequency tones to an individual. The multiple frequencies are used forpreventing numerous intermodulation products. However, the inventiondoes not provide any way of reducing other noise influences. Thesignal-to-noise ratio, while improved, is still low and therefore manyof the problems remain unchanged. U.S. Pat. No. 5,664,577 teaches asystem and a method for reducing the noise levels in the system bycollecting multiple readings for the intermodulation products and takingthe average value. Also, two microphones are used with a differentialamplifier for reducing the noise.

[0006] These and other solutions are plagued by many technical andclinical disadvantages. At present, most instruments for detection ofsignals in noise use signal processing methods which employ averaging inthe time domain and Fast Fourier Transforms (FFT).

[0007] Because of the need to average several time segments in thesemethods, there is a time delay before the signal is known. This delay iseven larger in the presence of artifacts. In the case of DPOAE artifactscan arise from irregular breathing, patient or operator movements, andenvironmental noise such as shutting of doors, sounds of equipment,steps of personnel and the like. Further, the averaged time signalcontains artifacts due to the time segmentation. These artifacts are tobe rejected from averaging, and therefore increase the delay. Also, theFFT data does not allow the signal to be monitored and output (or playedback) in real time.

[0008] The aforementioned technical factors cause clinicaldisadvantages, which decrease the clinical value of the present-daymethods. Because the signal can not be directly output, for example,DPOAEs cannot be output to a speaker. Therefore, they cannot be detectedand/or monitored by an operator. Because the signal can not be quicklyanalyzed when the frequencies of stimuli are varying in time, it is verytime consuming to obtain a frequency response of the signal, that is,its amplitude as a monotonous function of the stimulus frequency. Thiscan be important, for example, for DPOAEs because their amplitude variessignificantly with very little change in the frequencies of primarytones.

[0009] The use of averaging techniques allows the clinician to obtainthe signals only at certain times, and does not allow him/her tocontinually monitor the signal's level in time. In certain situations,this is critical. For example, during an operation on the acousticnerve, DPOAE level can indicate the physiological state of the cochleaand help prevent a cochlear catastrophe caused by interruption of bloodsupply. Another example is in titrating ototoxic drugs, DPOAE levelmonitoring can help prevent drug-induced cochlear injury.

[0010] Another example of physiological signal significantlycontaminated by noise is Auditory Steady State Response (ASSR). ASSR isan electric sinusoidal signal, supposedly originating in the brainstem,elicited by a modulated sinusoidal stimulus. The stimulus is typically acarrier tone of audible frequency range the amplitude or frequency ofwhich is modulated with low modulation frequency typically between 40and 100 Hz. The ASSR signal has exactly the frequency of suchmodulation, and very low amplitude, which causes difficulty for reliablyextracting it from noise.

[0011] The principle of ASSR measurement is described as follows. Amodulated pure tone is presented to the ear. The carrier frequencies areusually conventional audiometric tones, from 125 to 8000 Hz. The levelsof the frequencies are at or higher than 20 dB SPL. The modulationfrequencies are typically 40 Hz or within the 70 to 100 Hz range(usually 80 Hz if they are in the 70 to 100 Hz range), with a 0.95modulation index.

[0012] At the time of stimulation, a sinusoidal electric signal, whichhas the frequency equal to the modulation frequency of the stimulus,appears on the surface of the skull. This signal, supposedly produced bythe brainstem, is called the ASSR. The ASSR can be recorded from thesurface of the skull with three electrodes, typically on the vertex, onthe temporal bone, and on the lobule. This electric signal, whosemagnitude is typically from 40 to 400 nV, is then amplified with atypical gain of approximately 10,000 V/V. It is then passed through aband pass filter, with a typical lower frequency cutoff at 10 to 30 Hzand a higher frequency cutoff at 100 to 300 Hz. It is converted into itsdigital form and processed.

[0013] Techniques for ASSR detection suffer from the same drawbacks asthose for DPOAE; however, a particular disadvantage of ASSRs is thattheir detection with current signal processing techniques requires longrecording times.

[0014] It is an object of the present invention to obviate or mitigateat least some of the disadvantages discussed above.

SUMMARY OF THE INVENTION

[0015] The present invention provides a system for use in a real timesystem and for processing a signal with a low signal-to-noise ratio(SNR). The system comprises a model for modeling an expected signal anda filter that uses the model for filtering the signal. The filter isused for generating a prediction of the signal and an error variancematrix. The system further comprises an adaptive element for modifyingthe error variance matrix such that the bandwidth of the filter iswidened, wherein the filter behaves like an adaptive filter.

[0016] The present invention further provides a system for processing asignal with a low signal-to-noise ratio (SNR) for providing output to anoperator. The system comprises a model for modeling an expected signal,a filter using the model for filtering the signal for generating aprediction of the signal and an error variance matrix. The systemfurther comprises an adaptive element for modifying the error variancematrix such that the bandwidth of the filter is widened. A processor isprovided for processing the filtered signal for determining its signal-characteristics, and an output is used for providing the signalcharacteristics to the operator. The system provides the output to theoperator in real-time.

[0017] The present invention further provides a method for use in a realtime system and for processing a signal with a low signal-to-noise ratio(SNR). The method comprises the steps of modeling an expected signal,filtering the signal for generating a signal prediction and an errorvariance matrix, modifying said error variance matrix such that thebandwidth is widened, processing the filtered signal for determining thesignal characteristics, and providing the signal characteristics to anoperator. The method provides said output to the operator in real-time.

BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The invention will now be described by way of example withreference to the following drawings in which:

[0019]FIG. 1 is a block diagram of an ear testing system according to anembodiment of the invention

[0020]FIG. 2 is a flow diagram illustrating the operation of a Kalmanfilter;

[0021]FIG. 3 is a flow diagram illustrating the operation of an improvedKalman filter for use with DPOAE detection;

[0022]FIG. 4 is a flow diagram illustrating the operation of a furtherimproved Kalman filter for use with DPOAE detection incorporating adecay factor and a scale factor;

[0023]FIG. 5 is a flow diagram illustrating the operation of a etherimproved Kalman filter for use with DPOAE detection incorporating adecay factor, a scale factor and a variable step;

[0024]FIG. 6 is a block diagram of a sample output screen according to nembodiment of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0025] According to an embodiment of the invention, a method employs alinear minimum mean-square error filter, know as the Kalman Filter, forextracting sinusoidal physiological signals, such as DPOAEs and ASSRs,from noise is described. The method includes signal modeling and fastsignal processing algorithm. This method is also suitable for extractingany physiological signals of known frequency composition from backgroundnoise.

[0026] The method consists of the following steps. The signal ismodeled. For enabling the use of the Kalman Filter for DPOAE signalprocessing, several models are developed for different processing tasks.The include models that are suitable for processing time-invariantfrequency stimuli, models that are suitable for processing time-variantfrequency stimuli, models that are suitable for processing the signal inwhich there is a strong power line interference (for example, 50 Hz, or60 Hz interference), and models that suitable continuously setting areference (or threshold) level for DPOAE, and also for otherphysiological signals.

[0027] The properties of the signal model are used for reducing thenumber of computational operations and therefore processing time.Variable step sizes are used, which leads to faster iteration, andshorter processing time. Re-initialization of the filter is avoided byintroducing two parameters, which are referred to as a decay factor anda scale factor.

[0028] Several post-processing steps are also taken to maximizeefficiency and ensure accuracy. Automatic reference (Thresholding) isused for preventing false detection (sound level display). A method forpresenting estimation of DPOAEs as two-channel waveform output (audiooutput) is presented. A method for distinguishing ear-originateddistortion product (DPOAE) from distortion product created by therecording system (calibration method) is also introduced.

[0029] “Biological” detection of physiological signals is alsodescribed. Physiological signals, such as DPOAEs, are extracted fromnoise and are presented to and detected by an operator. If the signal'sfrequency is not in the audible frequency and dynamic range, it istransposed into the audible frequency range and amplified so that anoperator can comfortably hear it.

[0030] An embodiment of the invention will now be described in terms ofDPOAE recording.

[0031]FIG. 1 shows a system for testing hearing according to the presentembodiment of the invention, represented generally by the numeral 10.Two primary tones are generated electronically by computer controlledtone generators 12. The tones are presented by two speakers 14 into theoccluded car canal (the external auditory meatus) 16. Sounds in the earcanal 16 are recorded by a microphone 18 and transformed into electricalsignals The speakers 14 and microphone 16 are typical contained in asingle device 17 for easy insertion into the ear. A low noise microphonepre-amplifier 19 amplifies the signal and an Analog-to-Digital (A/D)converter 20 transforms the electrical signal into its digital form. Thedigital signal is processed by a processor 22 for extracting the DPOAEboth from the primaries and the noise. The DPOAE signal is analyzed by aprocessor 24, displayed by a display device (such as a monitor or thelike) 26, and recorded on data storage (disk drive, CD-ROM, or the like)28 in its digital form. These devices are typically contained in acomputer 30. The processed DPOAE signal is transformed into its analogform by an Analog-to-Digital (AID) converter 32 for analysis, display(visual or audio), and recording. An operator 34 wears a headset 36 witha pair of headphones 36 a and 36 b for listening to the analog signal.

[0032] The two primary tones have frequencies f₁ and f₂ (f₁<f₂), andlevels L₁ and L₂. Typically, the frequency f₂ is between 500 Hz and 10kHz, the ratio f₂/f₁ is between 1.2 and 1.25, and the ratio L₁/L₂ isbetween minus 10 and plus 10 dB. The sounds recorded by the microphone18 contain the two primary tones, physiological and background noise,and the Distortion Product Otoacoustic Emissions (if they are present).The strongest component of the sounds has the frequency 2f₁-f₂.

[0033] For digital signal processing, a linear mean-square error filteris used. The filter used is a Kalman Filter, which is a known approachto filtering, but it has not been previously used for the purpose ofseparation of DPOAEs from stimuli, and the extraction of the signalsfrom noise.

[0034] The basics of the Kalman Filter are the following:

[0035] Given the following state model and observation model

x[k+1]=G[k]x[k]+w[k]  (1)

z[k]=H[k]x[k]+v[k]  (2)

[0036] where

[0037] x[k] is m×1 state vector,

[0038] G[k] is m×m state matrix of constants, which is a description ofthe mth order difference equation model of the siganl.

[0039] w[k] is m×1 vector sequence of Gausian white noise uncorrelatedwith both x[0] and v[k]

[0040] z[k] is n×1 observation vector

[0041] H[k] is n×m matrix of constants describing the relationshipbetween the state vector and the observation vector.

[0042] v[k] is m×1 vector sequence of Gausian white noise uncorrelatedwith both x[0] and w[k]

[0043] x[0] m×1 initial state vector; a zero mean Gaussian randomvariable with convariance matrix P[0]

[0044] the covariance matrices of w and v are assumed to be known andhave the form of $\begin{matrix}\begin{matrix}{{E\left\lbrack {{w\lbrack j\rbrack}{w^{T}\lbrack k\rbrack}} \right\rbrack} = {Q\lbrack k\rbrack}} & {j = k} \\{= 0} & {j \neq k}\end{matrix} & (3) \\\begin{matrix}{{E\left\lbrack {{v\lbrack j\rbrack}{v^{T}\lbrack k\rbrack}} \right\rbrack} = {R\lbrack k\rbrack}} & {j = k} \\{= 0} & {j \neq k}\end{matrix} & (4)\end{matrix}$

[0045]FIG. 2 illustrates the algorithm for the Kalman Filter, whichgenerates a Linear Minimum Mean Square Error Estimation of x.

[0046] The signal processing method is based on signal modeling. Belowis provided an example for illustrating the details of the variousmodeling methods. If it is assumed that the measured signal consists ofseveral major frequency elements (for example, indicated as f₁, f₂, f₃,. . . ), and these elements have different physical meanings, it ispossible to obtain different models:

[0047] In the case of DPOAE testing, let f₁, f₂, f₃ be Primary One,Primary Two, and DPOAE respectively, and let z be the measured data fromthe, microphone and amplifier. Then obtain a model that can be used forprocessing the DPOAE signal (to be described below). In this model thestate vector x has six elements, x₀, x₁, x₂, x₃, x₄, x₅, where x₀ and x₁are related to f1; x₂, x₃ are related to f₂; x₄ and x₅ are related tof₃. In this model, f₁, f₂, and f₃ are time invariant. Thus the model issuitable for processing time-invariant frequency stimuli.

[0048] If f₁, f₂, f₃, . . . are frequencies that can be changed withtime, the model becomes one that is suitable for time-variant frequencystimuli.

[0049] If one of f₁, f₂, f₃, . . . is equal to 60 (Hz) or 50 (Hz), thena model is obtained that is suitable for processing measured data whichare contaminated by 60Hz (or 50 Hz) power line interference.

[0050] If f₁, f₂, and f₃ are the major elements in the measured data, f₄can be a reference signal frequency which can be used to set a certainreference threshold. For example, in DPOAE testing, if the frequenciesare set as f₁=Primary One frequency, f₂=Primary Two frequency, f₃=DPOAEfrequency, and f₄=reference frequencies that are different from f₃, butvery close to f₃ and slowly change with time, then f₄ can be used forcontinually setting the noise reference threshold for DPOAE measurement.

[0051] If f₁=frequency of ASSR, f₂=60 (Hz), f₃=reference frequency amodel that can be used to process the ASSR signal is obtained.

[0052] The following description details the DPAOE signal processing.The processing methods described below can be used directly in the abovementioned models.

[0053] (a) Signal Modeling:

x[k+1]=G[k]x[k]+w[k]  (1)

z[k]=Hx[k]+v[k]  (2) ${G\lbrack k\rbrack} = \begin{bmatrix}G_{1} & 0 & 0 \\0 & G_{2} & 0 \\0 & 0 & G_{3}\end{bmatrix}$ $G_{i} = {\begin{bmatrix}{\cos \quad \left( {2\pi \quad {f_{i}/f_{s}}} \right)} & {- {\sin \left( {2\pi \quad {f_{i}/f_{s}}} \right)}} \\{\sin \left( {2\pi \quad {f_{i}/f_{s}}} \right)} & {\cos \left( {2\pi \quad {f_{i}/f_{s}}} \right)}\end{bmatrix}\quad \left( {{i = 1},2,3} \right)}$

[0054] f₁,f₂ are stimuli frequencies and f₃ is DPOAE frequency.

[0055] f_(s) is sampling frequency of the A/D converter$H = \begin{bmatrix}1 & 0 & 1 & 0 & 1 & 0\end{bmatrix}$ ${E{\langle{ww}^{T}\rangle}} = \begin{bmatrix}Q_{1} & 0 & 0 \\0 & Q_{2} & 0 \\0 & 0 & Q_{3}\end{bmatrix}$ $Q_{i} = {\begin{bmatrix}q_{i} & 0 \\0 & q_{i}\end{bmatrix}\quad \left( {{i = 1},2,3,{q_{i} \geq 0}} \right)}$

E<vv ^(T) >=r (r≧0) $x = \begin{bmatrix}{x1} \\{x2} \\{x3}\end{bmatrix}$ $x_{i} = {\begin{bmatrix}x_{{2i} - 2} \\x_{{2i} - 1}\end{bmatrix}\quad \left( {{i = 1},2,3} \right)}$

[0056] (b) Signal Processing Implementation:

[0057] denote Kalman Gain as k=[k_(i)]_((6×1))

[0058] denote estimation error variance matrix as P=[p_(ii]) _((6×6))

[0059] its block form is written as $P = \begin{bmatrix}p_{11} & p_{12} & p_{13} \\p_{21} & p_{22} & p_{23} \\p_{31} & p_{32} & p_{33}\end{bmatrix}$

[0060] The signal model has a special structure. Using the definition ofthe H matrix provided above, and the fact that P is a symmetric matrix,a substantial amount of multiplication in matrix computation can beavoided, thus increasing the processing speed. FIG. 2 illustrates analgorithm implementing this speed up.

[0061] The Kalman Filter by its nature is not an adaptive filter. Whenthe real signal does not fit the model or when the filter has alreadygone into steady state. the filter output cannot reflect the real signalchange. The filter must, therefore, be reinitiated otherwise the outputstays in an incorrect state. This is a general problem when using KalmanFilters. The re-initialization method is not preferable in the presentembodiment. This is because periodic re-initialization causessignificant clicks. This problem is avoided by introducing an algorithmto control the model error.

[0062] Two parameters, herein referred to as a decay factor and a scalefactor are, are defined. The decay factor is represented as λ(0≦λ≦1),and the scale factor as θ(0≦θ<<1). FIG. 4 illustrates the algorithm fora Kalman figure incorporating these factors. The values of λ and θmodify the value of the error variance matrix, P. The factors are usedtogether for widening the bandwidth of the filter. If fast testing speedis needed, the decay factor should be small. Similarly, if high accuracyis needed, the scale factor should be small. With this procedure thefilter can track the sudden signal change without re-initialization, andkeep accuracy at the same time.

[0063] In the algorithm shown in FIG. 4, the Kalman Gain and P matrixare updated in each iteration. The most time-consuming part of thecomputation in one iteration is updating the P matrix. It was noticedthat after the loop started, the gain, K, gradually becomes steady. Thischaracteristic is used to form an approximation algorithm. The fist stepin the procedure is to calculate delta_K, where:

delta_(—) K=∥K(n)−K(n−1)∥.

[0064] If delta_K>t (where t is a threshold), then,

Step=[1+Step_factor*∥k(n−1)∥/delta_(—) K].

[0065] In this case, the filter may not update P and Gain in eachiteration, only the estimation {acute over (x)} is updated. After{circumflex over (x)} is updated a number of times (or “Step” times),new gain, P and Step are computed. The threshold, t, is to preventcomputation overflow. The Step_factor is to control speed. When highcomputation speed is required, a large Step_factor should be chosen. Forhigh accuracy, a small Step_factor should be chosen.

[0066] If delta_K≦t, then Step=MAX_step (where MAX_step is the largeststep size) and the speed is maximized. This procedure is particularlyuseful with slow computers. FIG. 5 illustrates the modifications to theprevious algorithm for implementing his procedure.

[0067] The post-processing of a DPOAE signal has several coals. It isdesirable to make the processed data more understandable for operatorswho may not be very familiar with the details of signal processing.Further, it is important to make the testing result more reliable (i.e.minimize false detection etc.). Finally, it is useful to transfer theKalman Filter output to certain forms which operators can use for makingtheir decisions easily.

[0068] All the useful information that the Kalman Filter can provide iscontained in the estimation vector x. However, to the instrumentoperator, the information contained in x is not obvious. For theoperator to use this information easily it is necessary to convert it tosome form that is meaningful to the operators. The following areexamples of some of the post-processing procedures.

[0069] One example is the use of an indicator for showing the level ofprimaries and DPOAEs. The levels are defined as:

[0070] Primary One (f₁) level:

L ₁=10 log(x ₀ ² +x ₁ ²)/ref_amplitude²

[0071] Primary Two (f₂) level:

L ₂=10 log(x ₂ ² +x ₃ ²)/ref_amplitude²

[0072] DPOAE (f₃) level:

L ₃=10 log(x ₄ ² +x ₅ ²)/ref_amplitude²

[0073] Noise Level:

L _(noise)=10 log(z−x ₀ −x ₂ −x ₄)²/ref_level

[0074] where ref_amplitude is a value that correspond to 0 dB SPL. Thisvalue is determined by calibration FIG. 6 illustrates a sample outputscreen represented generally by the numeral 50. The screen 50 has bothlevel bars 52 and numerical indicators 54 for displaying these levels.

[0075] Automatic reference (or thresholding) method can be used to limitor prevent false signal detection. The DPOAE level is denoted as L₃, theDPOAE threshold as L_DP_THR, the system DP-limit as L_DP_system, themeasured noise level as L_noise, and the noise limit as L_noise_limit.Decisions regarding the origin of the detected signal are based on thefollowing comparisons. If L_noise>L_noise_limit, or if L₃<L_DP_system,then a decision cannot be made regarding the origin of the signal (i.e.,the DP is may originate not from the cochlea, but most likely from therecording system), and the result is not reliable.

[0076] If L₃>L_DP_system and L_noise<L_noise_limit, then a furthercomparison is required. That is, if L_>L_DP_THR, then it is confirmedthat there is a DPOAE. If L₃<L_DP_THR then it is confirmed that there isno DPOAE.

[0077] There are two ways for setting L_DP_THR. The first is to separatethe instrument operation session into two parts, which is shown in FIG.6. By pressing the “task control button” <F9> 56, the instrument can beswitched between the “Normal Testing Mode” and the “Threshold SettingMode”. When the system works in the “Threshold Setting Mode”, f₃ is setto frequencies that are close to but not equal to the DPOAE frequency,2f₁-f₂. The system performs measurements and updates the indicator ofL_DP_THR 58 and L_noise_limit 60. When this indicator becomes stablethen the operator can switch the system to work in the “Normal TestingMode”.

[0078] The second way for setting the L_DP_THR is by continually settingit. This is accomplished by adding a reference frequency component tothe basic DPOAE model, as previously described. In this case, theoperator does not need to switch the instrument to a different workingmode and is, therefore, convenient for the operators. The associatedcost is the extra computations required.

[0079] L_DP_system is a parameter that is related to the linearity ofthe overall system (from speaker to the microphone, amplifier and AIDconverter). This parameter can be set by calibration, which is definedfurther on.

[0080] The DPOAE estimation is further presented as a two-channel audiooutput. Two signals are formed based on Kalman Filter estimation andoutput through a two-channel audio output. The Channel One signal is

S ₁ :S1[k]=output_volume×{x ₄ [k]+α×(z[k]−x ₀ [k]−x ₂ [k]−x ₄ [k])}

[0081] And the Channel Two signal is

S ₂ :S1[k]=output_volume×{x ₅ [k]+a×(z[k]−x ₀ [k]−x ₂ [k]−x ₄ [k])}

[0082] In the above equations, (z[k]−x₀[k]−x₂[k]−x₄[k]) is used as areference signal. It is a wide-band signal. α(0<α<1) is a parameter thatcontrols the amplitude of the reference signal, It is preferred, but notrequired, that a is between 0.1 and 0.2.

[0083] The reason for adding a reference signal to output sound is thatx₄[k] is an optimal estimation of DPOAE, that is it has a highsignal-to-noise ratio, and together with x₅[k] it is used for obtainingan estimation of the intensity of the DPOAB. However, the human ear ismore sensitive to frequency difference than intensity difference.Therefore, a wide band reference signal is added for making thecomposite signal much easier for the operator to listen to and detectwhether or not there is a DPOAE signal present.

[0084] A calibration method is used in the decision-making proceduredescribed above. In order to make the clinical testing reliable, thedistortion caused by the recording system must be taken into account.The following is a sample procedure that can be used. Present two tonesinto a cavity (instead of the ear canal), and use the Kalman Filteralgorithm to estimate the “signal” level at the frequency of theexpected DPOAE, and store this level as L_DP_system. L_DP_system is afunction of both intensities (L₁ and L₂) and frequencies (f₁ and f₂) ofthe two-tone stimulus, therefore an array of data obtained at differentvalues of the stimulus intensities and frequencies should be used. Thiscalibration can be done on-line or off-line.

[0085] The human ear has an extraordinary ability to detect sounds inpresence of background noise. This is utilized in the detection ofDPOAEs. The signal containing DPOAEs (if they are present), separatedfrom the primaries and extracted from noise by the above describedsignal processing, is converted to analogue form and presented to anoperator via loudspeakers or headphones. The operator can then detectDPOAEs wit his or her own ears. This allows the operator to make fastanalysis of whether or not DPOAEs are present in the ear tested.

[0086] Detection of signals, like DPOAEs, by an operator can simplifythe testing procedure and device by eliminating readouts, print-outsetc., and thus significantly decrease the cost of both testing andreporting its results. This is not possible with present-day methods.

[0087] Detection by a computer of DPOAEs is not preferred because thereis always a distortion product present, which is produced by therecording system. It is difficult to distinguish between the two signalswhen the DPOAE level is at, or below, the level of the system's owndistortion product. However, it is possible that a computer can do thisanalysis.

[0088] The method described provides several advantages. The signalprocessing method can generate a real time estimation of severalparameters and waveforms at the same time. These include the level ofthe stimuli, the level of the response signal, the level of the noise,the waveform of stimuli, and the waveform of the response signal.Furthermore, no FFT is needed.

[0089] Since the primaries have already been removed from the DPwaveform, no further filtering is needed. It can directly output fromthe signal processor, for example, to a speaker for an operator tolisten to. In addition, all waveforms of the signal and stimuli (DPOAE,and the two primaries) consist of pairs of signals in quadrature (thatis, 90 degree phase difference). This may be helpful for setting outcriteria for screening purposes.

[0090] The system has the potential for being used in situations wherethe frequencies of stimuli are time variant. For that, all that isrequired is to form a G(k) that changes with time. The remainder of thealgorithm remains the same. This not only allows the measurements ofresponse signal at fixed frequencies of the stimuli, but also for acontinual sweep of the stimuli over a frequency range, thus obtainingthe frequency response of the signal as a monotonous function of thestimuli. Furthermore, the system has the potential for being used insituations where there is no stimulus signal at all. All that isrequired is a low SNR signal that can be modeled.

[0091] The algorithm can also be useful in other applications where asignal of known frequency composition must be detected with poorsignal-to-noise ratios.

[0092] For example, in an alternate embodiment the system allows forcontinuous monitoring of signal levels in real time. Monitoring ofsignal levels may be useful during surgery. For example, monitoring theDPOAE levels during surgery on the auditory nerve, or in titratingototoxic drugs, allows the operator to continuously monitor thephysiological status of cochlea. For such a case, the output to theoperator may not be in presented in a visual format. Rather, an alarmmay be sounded when a predetermined threshold is surpassed. Once again,this approach is not limited to monitoring DPOAE level but may beextended to any other signal that would be useful to monitor, has a lowSNR, and can be modeled.

[0093] Furthermore, since the system allows for continuous monitoring ofthe system in real time it may be used to calibrate devices such ashearing aids. pacemakers, eyeglasses and the like.

[0094] In a further embodiment, this method of signal processing can beused in immittance (impedance) audiometry (tympanometry and acousticreflex measurements), where the middle ear is probed with a probe puretone. The tone is typically of the frequency 226 Hz, in which theintensity of the tone can be reduced, without reduction of sensitivity,in order to reduce the patient's discomfort due to the probe tone. Aswell, the influence of acoustic artifacts can be reduced.

[0095] In yet a farther embodiment, the method of Signal processing canalso be used in testing hearing aids, especially with low-level inputsignals, in order to decrease the influence of acoustic artifacts.

[0096] The method of signal processing and detection can also be used inrecording and analyzing of many other physiological signals, forexample, cardiac, visual, nervous and the like.

[0097] Physiological signals, such as DPOAEs and ASSRs, can begeneralized as part of a class of signals that have known or expectedfrequencies, and are present in significant background noise. In orderto detect such signals, it is necessary to perform signal processing.Although this description refers only to DPOAEs and ASSRs, it can easilybe extended to the whole class of signals described above by a personskilled in the art. Furthermore, although the preferred embodimentsrefer only to use with a Kalmam filter a person skilled in the art couldextend the application to include other filters.

The embodiments of the invention in which an exclusive property orprivilege is claimed are defined as follows:
 1. A signal processor foruse in a real time system and for processing a signal with a lowsignal-to-noise ratio (SNR) comprising: a model for modeling an expectedsignal; a filter using said model for filtering said signal forgenerating a prediction of said signal and an error variance matrix; andan adaptive element for modifying said error variance matrix such thatthe bandwidth of said filter is widened; wherein said filter behaveslike an adaptive filter.
 2. A signal processor as defined in claim 1,wherein said filter is a Kalman filter
 3. A signal processor as definedin claim 2, wherein said Kalman filter is digital.
 4. A system forprocessing a signal with a low signal-to-noise ratio (SNR) for providingoutput to an operator comprising: a model for modeling an expectedsignal; a filter using said model for filtering said signal forgenerating a prediction of said signal and an error variance matrix; andan adaptive element for modifying said error variance matrix such thatthe bandwidth of said filter is widened; a processor for processing saidfiltered signal for determining signal characteristics of said signal;an output for providing said signal characteristics to said operator;wherein said system provides said output to said operator in real-time.5. A system as defined in claim 4, wherein said filter is a Kalmanfilter.
 6. A system as defined in claim 5, wherein said Kalman filter isdigital.
 7. A system as defined in claim 6, wherein said output isvisually presented to said operator.
 8. A system as defined in claim 7,wherein said visual output is displayed to said operator on monitor. 9.A system as defined in claim 6, further comprising a digital to analogconverter for providing said an analog signal to said operator, saidanalog signal dependent on said signal characteristics.
 10. A system asdefined in claim 9, wherein said analog output is an audio output.
 11. Asystem as defined in claim 10, wherein said analog output is a directrepresentation of said input signal.
 12. A system as defined in claim11, wherein the frequency of said audio output is adjusted such that itlies within the range of human hearing.
 13. A system as defined in claim11, wherein the amplitude of said audio output is increased.
 14. Asystem as defined in claim 10, wherein said audio output is used foralerting said operator that said input signal has passed a predeterminedthreshold.
 15. A system as defined in claim 5, wherein said input signalis a physiological signal.
 16. A system as defined in claim 15, whereinsaid physiological signal is a distinguishing ear-originated distortionproduct (DPOAE).
 17. A system as defined in claim 15, wherein saidphysiological signal is a auditory steady state response (ASSR).
 18. Amethod for processing a signal with a low signal-to-noise ratio (SNR)for providing output to an operator comprising: modeling an expectedsignal; filtering said signal for generating a prediction of said signaland an error variance matrix; modifying said error variance matrix suchthat the bandwidth of said filter is widened; processing said filteredsignal for determining signal characteristics; providing said signalcharacteristics to said operator; wherein said method provides saidoutput to said operator in real-time.
 19. A method as defined in claim18 wherein said filtering is performed by a Kalman filter.
 20. A methodas defined in claim 19, wherein said Kalman filter is a digital filter.21. A method as defined in claim 19, further comprising the steps of:converting said processed digital response signal to an analog format;providing said analog processed response signal to an operator.
 22. Amethod as defined in claim 21, wherein said analog output is an audiooutput.
 23. A method as defined in claim 22, wherein said analog outputis a direct representation of said input signal.
 24. A method as definedin claim 23, wherein the frequency of said audio output is adjusted suchthat it lies within the range of human hearing.
 25. A method as definedin claim 24, wherein the amplitude of said audio output is increased.26. A method as defined in claim 22, wherein said audio output is usedfor alerting said operator that said input signal has passed apredetermined threshold.
 27. A method as defined in claim 19, whereinsaid physiological signal is a distinguishing ear-originated distortionproduct (DPOAE).
 28. A method as defined in claim 19, wherein saidphysiological signal is a auditory steady state response (ASSR).